Package ‘BAwiR’of their respectives robots.txt ﬁles, when available. Tools for visualization include a popula-tion pyramid, 2D plots, circular plots of players' percentiles,

Package ‘BAwiR’May 11, 2020

Type Package

Title Analysis of Basketball Data

Version 1.2.4

Date 2020-05-11

Author Guillermo Vinue

Maintainer Guillermo Vinue <[email protected]>

Description Collection of tools to work with basketball data. Functions available are related to friendlyweb scraping and visualization. Data were obtained from <http://www.euroleague.net/>,<http://www.eurocupbasketball.com/> and <http://www.acb.com>, following the instructionsof their respectives robots.txt files, when available. Tools for visualization include a popula-tion pyramid, 2D plots,circular plots of players' percentiles, plots of players' monthly/yearly stats,team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season gamesand maps of nationalities. Please see Vinue (2020) <doi:10.1089/big.2018.0124>.

License GPL (>= 2)

URL https://www.R-project.org, https://www.uv.es/vivigui,

https://www.uv.es/vivigui/AppEuroACB.html

Depends R (>= 3.4.0)

Imports Anthropometry, plyr, dplyr, ggplot2, ggthemes, grid,hrbrthemes, httr, lubridate, magrittr, purrr, reshape2, rvest,rworldmap, scales, stringi, stringr, tibble, tidyr, xml2

Suggests knitr

VignetteBuilder knitr

LazyData true

RoxygenNote 6.1.1

NeedsCompilation no

Repository CRAN

Date/Publication 2020-05-11 08:40:02 UTC

1

https://www.R-project.org

https://www.uv.es/vivigui

https://www.uv.es/vivigui/AppEuroACB.html

2 R topics documented:

R topics documented:

BAwiR-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3acb_games_1718 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4acb_players_1718 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5capit_two_words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5do_add_adv_stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6do_EPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7do_four_factors_df . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8do_join_games_bio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9do_map_nats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10do_OE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11do_scraping_games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12do_scraping_rosters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13do_stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14do_stats_teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15eurocup_games_1718 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16eurocup_players_1718 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16euroleague_games_1718 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17euroleague_players_1718 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17get_barplot_monthly_stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18get_bubble_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19get_four_factors_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21get_games_rosters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22get_heatmap_bb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25get_map_nats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26get_pop_pyramid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27get_shooting_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28get_similar_players . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29get_similar_teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30get_stats_seasons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31get_table_results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32join_players_bio_age_acb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33join_players_bio_age_euro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34scraping_games_acb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35scraping_games_euro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36scraping_rosters_acb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38scraping_rosters_euro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Index 41

BAwiR-package 3

BAwiR-package Analysis of Basketball Data

Description

Collection of tools to work with basketball data. Functions available are related to friendly webscraping and visualization. Data were obtained from <http://www.euroleague.net/>, <http://www.eurocupbasketball.com/>and <http://www.acb.com>, following the instructions of their respectives robots.txt files, whenavailable. Tools for visualization include a population pyramid, 2D plots, circular plots of players’percentiles, plots of players’ monthly/yearly stats, team heatmaps, team shooting plots, team fourfactors plots, cross-tables with the results of regular season games and maps of nationalities. Pleasesee Vinue (2020) <doi:10.1089/big.2018.0124>.

Details

Package: BAwiRType: PackageVersion: 1.2.4Date: 2020-05-11License: GPL-2LazyLoad: yesLazyData: yes

acb_games_1718: ACB games 2017-2018.acb_players_1718: ACB players 2017-2018.capit_two_words: Capitalize two-word strings.do_add_adv_stats: Advanced statistics.do_EPS: Efficient Points Scored (EPS).do_four_factors_df: Four factors data frame.do_join_games_bio: Join games and players’ info.do_map_nats: Data frame for the nationalities map.do_OE: Offensive Efficiency (OE).do_scraping_games: Player game finder data.do_scraping_rosters: Players profile data.do_stats: Accumulated or average statistics.do_stats_teams: Accumulated and average statistics for teams.eurocup_games_1718: Eurocup games 2017-2018.eurocup_players_1718: Eurocup players 2017-2018.euroleague_games_1718: Euroleague games 2017-2018.euroleague_players_1718: Euroleague players 2017-2018.get_barplot_monthly_stats: Barplots with monthly stats.get_bubble_plot: Basketball bubble plot.get_four_factors_plot: Four factors plot.get_games_rosters: Get all games and rosters.get_heatmap_bb: Basketball heatmap.

4 acb_games_1718

get_map_nats: Nationalities map.get_pop_pyramid: ACB population pyramid.get_shooting_plot: Shooting plot.get_similar_players: Similar players to archetypoids.get_similar_teams: Similar teams to archetypoids.get_stats_seasons: Season-by-season stats.get_table_results: League cross table.join_players_bio_age_acb: Join ACB games and players’ info.join_players_bio_age_euro: Join Euroleague and Eurocup games and players’ info.scraping_games_acb: ACB player game finder data.scraping_games_euro: Euroleague and Eurocup player game finder data.scraping_rosters_acb: ACB players’ profile.scraping_rosters_euro: Euroleague and Eurocup players’ profile.

Author(s)

Guillermo Vinue <[email protected]>

References

Vinue, G., (2019). A web application for interactive visualization of European basketball data.Submitted, https://www.uv.es/vivigui/AppEuroACB.html

acb_games_1718 ACB games 2017-2018

Description

Games of the first seventeen days of the ACB 2017-2018 season.

Usage

acb_games_1718

Format

Data frame with 3939 rows and 38 columns.

Source

http://www.acb.com/

http://www.acb.com/

acb_players_1718 5

acb_players_1718 ACB players 2017-2018

Description

Players corresponding to the games of the first seventeen days of the ACB 2017-2018 season.

Usage

acb_players_1718

Format


Source

http://www.acb.com/

capit_two_words Capitalize two-word strings

Description

Ancillary function to capitalize the first letter of both words in a two-word string. This can be usedfor example to capitalize the teams names for the plots title.

Usage

capit_two_words(two_word_string)

Arguments

two_word_string

Two-word string.

Value

Vector with the two words capitalized.

Author(s)

Guillermo Vinue

Examples

capit_two_words("valencia basket")

http://www.acb.com/

6 do_add_adv_stats

do_add_adv_stats Advanced statistics

Description

This function adds to the whole data frame the advanced statistics for every player in every game.

Usage

do_add_adv_stats(df)

Arguments

df Data frame with the games and the players info.

Details

The advanced statistics computed are as follows:

• GameSc: Game Score.

• PIE: Player Impact Estimate.

• EFGPerc: Effective Field Goal Percentage.

• ThreeRate: Three points attempted regarding the total field goals attempted.

• FRate: Free Throws made regarding the total field goals attempted.

• STL_TOV: Steal to Turnover Ratio.

• AST_TOV: Assist to Turnover Ratio.

• PPS: Points Per Shot.

• OE: Offensive Efficiency.

• EPS: Efficient Points Scored.

The detailed definition of some of these stats can be found at https://www.basketball-reference.com/about/glossary.html and https://stats.nba.com/help/glossary/.

Value

Data frame.

Author(s)

Guillermo Vinue

See Also

do_OE, do_EPS

https://www.basketball-reference.com/about/glossary.html

https://www.basketball-reference.com/about/glossary.html

https://stats.nba.com/help/glossary/

do_EPS 7

Examples

df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)

do_EPS Efficient Points Scored (EPS)

Description

A limitation of do_OE is that it doesn’t rely on the quantity of the player’s offense production, that’sto say, whether the player provides a lot of offense or not. In addition, it does not give credit forfree-throws. An extension of do_OE has been defined: the Efficient Points Scored (EPS), which isthe result of the product of OE and points scored. Points scored counts free-throws, two-point andthree-point field goals. A factor F is also added to put the adjusted total points on a points scoredscale. With the factor F, the sum of the EPS scores for all players in a given season is equal to thesum of the league total points scored in that season.

Usage

do_EPS(df)

Arguments


Value

EPS values.

Author(s)

Guillermo Vinue

References

Shea, S., Baker, C., (2013). Basketball Analytics: Objective and Efficient Strategies for Under-standing How Teams Win. Lake St. Louis, MO: Advanced Metrics, LLC.

See Also

do_OE, do_add_adv_stats

Examples

df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)do_EPS(df1)[1]

8 do_four_factors_df

do_four_factors_df Four factors data frame

Description

This function computes team’s offense and defense four factors. The four factors are Effective FieldGoal Percentage (EFGP), Turnover Percentage (TOVP), Offensive Rebound Percentage (ORBP)and Free Throws Rate (FTRate). They are well defined at http://www.rawbw.com/~deano/articles/20040601_roboscout.htm and https://www.basketball-reference.com/about/factors.html.

As a summary, EFGP is a measure of shooting efficiency; TOVP is the percentage of possessionswhere the team missed the ball, see http://www.nba.com/thunder/news/stats101.html to readabout the 0.44 coefficient; ORBP measures how many rebounds were offensive from the total ofavailable rebounds; Finally, FTRate is a measure of both how often a team gets to the line and howoften they make them.

Usage

do_four_factors_df(df_games, teams)

Arguments

df_games Data frame with the games, players info, advanced stats and eventually recodedteams names.

teams Teams names.

Details

Instead of defining the Offensive and Defensive Rebound Percentage as mentioned in the previouslinks, I have computed just the Offensive Rebound Percentage for the team and for its rivals. Thismakes easier to have four facets, one per factor, in the ggplot.

In order to establish the team rankings, we have to consider these facts: In defense (accumulatedstatistics of the opponent teams to the team of interest), the best team in each factor is the onethat allows the smallest EFGP, the biggest TOVP, the smallest ORBP and the smallest FTRate,respectively.

In offense (accumulated statistics of the team of interest), the best team in each factor is the one thathas the biggest EFGP, the smallest TOVP, the biggest ORBP and the biggest FTRate, respectively.

Value

A list with two data frames, df_rank and df_no_rank. Both have the same columns:

• Team: Team name.• Type: Either Defense or Offense.• EFGP, ORBP, TOVP and FTRate.

The df_rank data frame contains the team ranking label for each statistic between parentheses.Therefore, df_no_rank is used to create the ggplot with the numerical values and df_rank is usedto add the ranking labels.

http://www.rawbw.com/~deano/articles/20040601_roboscout.htm

http://www.rawbw.com/~deano/articles/20040601_roboscout.htm

https://www.basketball-reference.com/about/factors.html

http://www.nba.com/thunder/news/stats101.html

do_join_games_bio 9

Author(s)

Guillermo Vinue

See Also

get_four_factors_plot

Examples

df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)# When only one team is selected the rankings between parentheses# do not reflect the real rankings regarding all the league teams.# The rankings are computed with respect to the number of teams# passed as an argument.df_four_factors <- do_four_factors_df(df1, "Valencia")

do_join_games_bio Join games and players’ info

Description

This function calls the needed ancillary functions to join the games played by all the players in thedesired competition (currently ACB, Euroleague and Eurocup) with their personal details.

Usage

do_join_games_bio(competition, df_games, df_rosters)

Arguments

competition String. Options are "ACB", "Euroleague" and "Eurocup".

df_games Data frame with the games.

df_rosters Data frame with the biography of the roster players.

Value

Data frame.

Author(s)

Guillermo Vinue

See Also

join_players_bio_age_acb, join_players_bio_age_euro

10 do_map_nats

Examples

df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)

do_map_nats Data frame for the nationalities map

Description

This function prepares the data frame with the nationalities to be mapped with get_map_nats. It isused inside it.

Usage

do_map_nats(df_stats)

Arguments

df_stats Data frame with the statistics and the corrected nationalities.

Value

List with the following elements:

• df_all: Data frame with each country, its latitudes and longitudes and whether it must becoloured or not (depending on if there are players from that country).

• countr_num: Vector with the countries from where there are players and the number of them.

• leng: Number of countries in the world.

Author(s)

Guillermo Vinue

See Also

get_map_nats

do_OE 11

do_OE Offensive Efficiency (OE)

Description

Offensive Efficiency (OE) is a measure to evaluate the quality of offense produced. OE counts thetotal number of successful offensive possessions the player was involved in, regarding the player’stotal number of potential ends of possession.

This measure is used in the definition of do_EPS.

Usage

do_OE(df)

Arguments


Value

OE values.

Note

When either both the numerator and denominator of the OE expression are 0 or just the denominatoris 0, the function returns a 0.

Author(s)

Guillermo Vinue

References

Shea, S., Baker, C., (2013). Basketball Analytics: Objective and Efficient Strategies for Under-standing How Teams Win. Lake St. Louis, MO: Advanced Metrics, LLC.

See Also

do_EPS, do_add_adv_stats

Examples

df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)# Players with OE = 0:# df1[55, c("Player.x", "FG", "AST", "FGA", "ORB", "TOV")]# Player.x FG AST FGA ORB TOV# Triguero, J. 0 0 0 0 0

12 do_scraping_games

# OE can be greater than 1, for example:# df1[17, c("Player.x", "FG", "AST", "FGA", "ORB", "TOV")]# Player.x FG AST FGA ORB TOV# Diagne, Moussa 3 0 3 1 0do_OE(df1[1,])

do_scraping_games Player game finder data

Description

This function calls the needed ancillary functions to scrape the player game finder data for thedesired competition (currently, ACB, Euroleague and Eurocup).

Usage

do_scraping_games(competition, type_league, nums, year, verbose, accents, r_user)

Arguments


type_league String. If competition is ACB, to scrape ACB league games ("ACB"), Copadel Rey games ("CREY") or Supercopa games ("SCOPA").

nums Numbers corresponding to the website from which scraping.

year If competition is either Euroleague or Eurocup, the year when the season startsis needed. 2017 refers to 2017-2018 and so on.

verbose Should R report information on progress? Default TRUE.

accents If competition is ACB, should we keep the Spanish accents? The recom-mended option is to remove them, so default FALSE.

r_user Email to identify the user when doing web scraping. This is a polite way to doweb scraping and to certify that the user is working as transparently as possiblewith a research purpose.

Value

A data frame with the player game finder data for the competition selected.

Author(s)

Guillermo Vinue

See Also

scraping_games_acb, scraping_games_euro

do_scraping_rosters 13

Examples

## Not run:# Not needed to scrape every time the package is checked, built and installed.df1 <- do_scraping_games(competition = "ACB", type_league = "ACB", nums = 62001,

year = "2017-2018", verbose = TRUE, accents = FALSE,r_user = "[email protected]")

df1_eur <- do_scraping_games(competition = "Euroleague", nums = 1,year = "2017", verbose = TRUE,r_user = "[email protected]")

## End(Not run)

do_scraping_rosters Players profile data

Description

This function calls the needed ancillary functions to scrape the players’ profile data for the desiredcompetition (currently, ACB, Euroleague and Eurocup).

Usage

do_scraping_rosters(competition, pcode, verbose, accents, year, r_user)

Arguments


pcode Code corresponding to the player’s website to scrape.



year If competition is either Euroleague or Eurocup, the year when the season startsis needed. 2017 refers to 2017-2018 and so on.


Value

A data frame with the players’ information.

Author(s)

Guillermo Vinue

14 do_stats

See Also

scraping_games_acb, scraping_rosters_euro

Examples

## Not run:# Not needed to scrape every time the package is checked, built and installed.df_bio <- do_scraping_rosters(competition = "ACB", pcode = "56C",

verbose = TRUE, accents = FALSE,r_user = "[email protected]")

df_bio_eur <- do_scraping_rosters(competition = "Euroleague", pcode = "007969",year = "2017", verbose = TRUE,r_user = "[email protected]")

## End(Not run)

do_stats Accumulated or average statistics

Description

This function computes either the total or the average statistics.

Usage

do_stats(df_games, type_stats = "Total", season, competition, type_season)

Arguments


type_stats String. In English, the options are "Total" and "Average" and in Spanish, theoptions are "Totales" and "Promedio".

season String indicating the season, for example, 2017-2018.


type_season String with the round of competition, for example regular season or playoffs andso on.

Value

Data frame.

Author(s)

Guillermo Vinue

do_stats_teams 15

Examples

compet <- "ACB"df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)df2 <- do_stats(df1, "Total", "2017-2018", compet, "Regular Season")

do_stats_teams Accumulated and average statistics for teams

Description

This function computes the total and average statistics for every team.

Usage

do_stats_teams(df_games, season, competition, type_season)

Arguments


season String indicating the season, for example, 2017-2018.competition String. Options are "ACB", "Euroleague" and "Eurocup".type_season String with the round of competition, for example regular season or playoffs and

so on.

Value

A list with two elements:

• df_team_total: Data frame with the total statistics for every team.• df_team_mean: Data frame with the average statistics for every team.

Author(s)

Guillermo Vinue

Examples

compet <- "ACB"df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)df$Compet <- competdf_teams <- do_stats_teams(df, "2017-2018", "ACB", "Regular Season")# Total statistics:#df_teams$df_team_total# Average statistics:#df_teams$df_team_mean

16 eurocup_players_1718

eurocup_games_1718 Eurocup games 2017-2018

Description

Games of the ten days of regular season and the first three days of top 16 of the Eurocup 2017-2018season.

Usage

eurocup_games_1718

Format


Source

http://www.eurocupbasketball.com/

eurocup_players_1718 Eurocup players 2017-2018

Description

Players corresponding to the games of the ten days of regular season and the first three days of top16 of the Eurocup 2017-2018 season.

Usage

eurocup_players_1718

Format


Source




euroleague_games_1718 17

euroleague_games_1718 Euroleague games 2017-2018

Description

Games of the first nineteen days of the Euroleague 2017-2018 season.

Usage

euroleague_games_1718

Format


Source

http://www.euroleague.net

euroleague_players_1718

Euroleague players 2017-2018

Description

Players corresponding to the games of the first nineteen days of the Euroleague 2017-2018 season.

Usage

euroleague_players_1718

Format


Source




18 get_barplot_monthly_stats

get_barplot_monthly_stats

Barplots with monthly stats

Description

In all the available basketball websites, the stats are presented for the whole number of gamesplayed. This function represents a barplot with the players’ stats for each month, which is veryuseful to analyse the players’ evolution.

Usage

get_barplot_monthly_stats(df_stats, title, size_text = 2.5)

Arguments

df_stats Data frame with the statistics.

title Plot title.

size_text Label size for each bar. Default 2.5.

Value

Graphical device.

Author(s)

Guillermo Vinue

See Also

capit_two_words

Examples

## Not run:library(dplyr)compet <- "ACB"df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)

months <- c(df %>% distinct(Month))$Monthmonths_order <- c("September", "October", "November", "December",

"January", "February", "March", "April", "May", "June")months_plot <- match(months_order, months)months_plot1 <- months_plot[!is.na(months_plot)]months_plot2 <- months[months_plot1]

df3_m <- df1 %>%

get_bubble_plot 19

filter(Team == "Real_Madrid",Player.x == "Doncic, Luka") %>%

group_by(Month) %>%do(do_stats(., "Average", "2017-2018", "ACB", "Regular Season")) %>%ungroup() %>%mutate(Month = factor(Month, levels = months_plot2)) %>%arrange(Month)

stats <- c("GP", "MP", "PTS", "FGA", "FGPerc", "ThreePA","ThreePPerc", "FTA", "FTPerc","TRB", "ORB", "AST", "TOV", "STL")

df3_m1 <- df3_m %>%select(1:5, stats, 46:50)

get_barplot_monthly_stats(df3_m1, paste("; ACB", "2017-2018", "Average", sep = " ; "),2.5)

# For all teams and players:teams <- as.character(sort(unique(df1$Team)))df3_m <- df1 %>%filter(Team == teams[13]) %>%group_by(Month) %>%do(do_stats(., "Average", "2017-2018", "ACB", "Regular Season")) %>%ungroup() %>%mutate(Month = factor(Month, levels = months_plot2)) %>%arrange(Month)

df3_m1 <- df3_m %>%select(1:5, stats, 46:50)

for (i in unique(df3_m1$Name)) {print(i)print(get_barplot_monthly_stats(df3_m1 %>% filter(Name == i),

paste(" ; ACB", "2017-2018", "Average", sep = " ; "),2.5))

}

## End(Not run)

get_bubble_plot Basketball bubble plot

Description

This plot is a representation of the percentiles of all statistics for a particular player. The figureshows four cells. The first box contains the percentiles between 0 and 24. The second, between25 and 49. The third, between 50 and 74 and the fourth, between 75 and 100. The percentiles arecomputed with the function percentilsArchetypoid. Boxes of the same percentile category arein the same color in the interests of easy understanding.

20 get_bubble_plot

This type of visualization allows the user to analyze each player in a very simple way, since ageneral idea of those aspects of the game in which the player excels can be obtained.

Usage

get_bubble_plot(df_stats, player, descr_stats, size_text, size_text_x, size_legend)

Arguments


player Player.

descr_stats Description of the statistics for the legend.

size_text Text size inside each box.

size_text_x Stats labels size.

size_legend Legend size.

Details

In the example shown below, it can be seen that Alberto Abalde has a percentile of x in free throwspercentage. This means that the x percent of league players has a fewer percentage than him, whilethere is a (100-x) percent who has a bigger percentage.

Value

Graphical device.

Author(s)

This function has been created using the code from this website: https://www.r-bloggers.com/visualizing-the-best/.

See Also

percentilsArchetypoid

Examples

## Not run:compet <- "ACB"df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)df2 <- do_stats(df1, "Total", "2017-2018", compet, "Regular Season")# When choosing a subset of stats, follow the order in which they appear# in the data frame.stats <- c("GP", "MP", "PTS", "FGA", "FGPerc", "ThreePA", "ThreePPerc",

"FTA", "FTPerc", "TRB", "ORB", "AST", "STL", "TOV")df2_1 <- df2[, c(1:5, which(colnames(df2) %in% stats), 46:49)]descr_stats <- c("Games played", "Minutes played", "Points",

"Field goals attempted", "Field goals percentage","3-point field goals attempted", "3-point percentage",

https://www.r-bloggers.com/visualizing-the-best/

https://www.r-bloggers.com/visualizing-the-best/

get_four_factors_plot 21

"FTA: Free throws attempted", "Free throws percentage","Total rebounds", "Offensive rebounds","Assists", "Steals", "Turnovers")

get_bubble_plot(df2_1, "Abalde, Alberto", descr_stats, 6, 10, 12)

## End(Not run)

get_four_factors_plot Four factors plot

Description

Once computed the team’s factors and its rankings with do_four_factors_df, this function repre-sents them.

Usage

get_four_factors_plot(df_rank, df_no_rank, team, language)

Arguments

df_rank Data frame with the team’s offense and defense four factors and its rankinglabels.

df_no_rank Data frame with the team’s offense and defense four factors.

team Team name. Multiple teams can be chosen.

language Language labels. Current options are ’en’ for English and ’es’ for Spanish.

Value

Graphical device.

Author(s)

Guillermo Vinue

See Also

do_four_factors_df

22 get_games_rosters

Examples

## Not run:df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)team <- "Valencia"df_four_factors <- do_four_factors_df(df1, team)# If only one team is represented the ranking between parentheses is just one.get_four_factors_plot(df_four_factors$df_rank,

df_four_factors$df_no_rank, team, "en")

## End(Not run)

get_games_rosters Get all games and rosters

Description

This function is to get all the games and rosters of the competition selected.

Usage

get_games_rosters(competition, type_league, nums, verbose = TRUE,accents = FALSE, r_user, df0, df_bio0)

Arguments







df0 Data frame to save the games data.

df_bio0 Data frame to save the rosters data.

Value

Data frame.

Author(s)

Guillermo Vinue

get_games_rosters 23

Examples

## Not run:library(readr)# 1. The first time, all the historical data until the last games played can be# directly scraped.

# ACB seasons available and corresponding games numbers:acb_nums <- list(30001:30257, 31001:31262, 32001:32264, 33001:33492, 34001:34487,

35001:35494, 36001:36498, 37001:37401, 38001:38347, 39001:39417,40001:40415, 41001:41351, 42001:42350, 43001:43339, 44001:44341,45001:45339, 46001:46339, 47001:47339, 48001:48341, 49001:49341,50001:50339, 51001:51340, 52001:52327, 53001:53294, 54001:54331,55001:55331, 56001:56333, 57001:57333, 58001:58332, 59001:59331,60001:60332, 61001:61298,62001:62135)

names(acb_nums) <- paste(as.character(1985:2017), as.character(1986:2018), sep = "-")

df0 <- data.frame()df_bio0 <- data.frame(CombinID = NA, Player = NA, Position = NA,

Height = NA, Date_birth = NA,Nationality = NA, Licence = NA, Website_player = NA)

# All the games and players:get_data <- get_games_rosters(competition = "ACB", type_league = "ACB",

nums = acb_nums, verbose = TRUE, accents = FALSE,r_user = "[email protected]",df0 = df0, df_bio0 = df_bio0)

acb_games <- get_data$df0acb_players <- get_data$df_bio0write_csv(acb_games, path = "acb_games.csv")write_csv(acb_players, path = "acb_players.csv")

# 2. Then, in order to scrape new games as they are played, the df0 and df_bio0 objects are# the historical games and rosters:acb_nums <- list(62136:62153)names(acb_nums) <- "2017-2018"df0 <- read_csv("acb_games.csv", guess_max = 1e5)df_bio0 <- read_csv("acb_players.csv", guess_max = 1e3)get_data <- get_games_rosters(competition = "ACB", type_league = "ACB",

nums = acb_nums, verbose = TRUE, accents = FALSE,r_user = "[email protected]",df0 = df0, df_bio0 = df_bio0)

# -----

# ACB Copa del Rey seasons available and corresponding games numbers (rosters werealready downloaded with the ACB league):acb_crey_nums <- list(50001:50004, 51001:51007, 52001:52007, 53033:53039,

54033:54039, 55033:55040, 56033:56040, 57029:57036,58025:58032, 59038:59045, 60001:60008, 61001:61007,62001:62007, 63001:63007, 64001:64007, 65001:65007,66001:66007, 67001:67007, 68001:68007, 69001:69007,

24 get_games_rosters

70001:70007, 71001:71007, 72001:72007, 73001:73007,74001:74007, 75001:75007, 76001:76007, 77001:77007,78001:78007, 79001:79007, 80001:80007, 81001:81007)

names(acb_crey_nums) <- paste(as.character(1985:2016), as.character(1986:2017), sep = "-")

df0 <- data.frame()get_data <- get_games_rosters(competition = "ACB", type_league = "CREY",

nums = acb_crey_nums, verbose = TRUE, accents = FALSE,r_user = "[email protected]",df0 = df0, df_bio0 = NULL)

acb_crey_games <- get_data$df0write_csv(acb_crey_games, path = "acb_crey_games.csv")

# -----

# ACB Supercopa seasons available and corresponding games numbers (rosters werealready downloaded with the ACB league):acb_scopa_nums <- list(1001, 2001, 3001, 4001, 5001:5004, 6001:6004,

7001:7003, 9001:9003, 10001:10003, 11001:11003,12001:12003, 13001:13003, 14001:14003, 15001:15003,16001:16003, 17001:17003, 18001:18003, 19001:19003)

# I haven't found the data for the supercopa in Bilbao 2007 ; 8001:8003# http://www.acb.com/fichas/SCOPA8001.phpnames(acb_scopa_nums) <- c(paste(as.character(1984:1987), as.character(1985:1988), sep = "-"),

paste(as.character(2004:2006), as.character(2005:2007), sep = "-"),paste(as.character(2008:2018), as.character(2009:2019), sep = "-"))

df0 <- data.frame()get_data <- get_games_rosters(competition = "ACB", type_league = "SCOPA",

nums = acb_scopa_nums, verbose = TRUE, accents = FALSE,r_user = "[email protected]",df0 = df0, df_bio0 = NULL)

acb_scopa_games <- get_data$df0write_csv(acb_scopa_games, path = "acb_scopa_games.csv")

# -----

# Euroleague seasons available and corresponding games numbers:euroleague_nums <- list(1:128,

1:263, 1:250, 1:251, 1:253, 1:253, 1:188, 1:189,1:188, 1:188, 1:231, 1:231, 1:231, 1:229, 1:220,1:220, 1:275, 1:169)

names(euroleague_nums) <- 2017:2000


Height = NA, Date_birth = NA,Nationality = NA, Website_player = NA)

get_data <- get_games_rosters(competition = "Euroleague", nums = euroleague_nums,verbose = TRUE, r_user = "[email protected]",df0 = df0, df_bio0 = df_bio0)

euroleague_games <- get_data$df0euroleague_players <- get_data$df_bio0

get_heatmap_bb 25

write_csv(euroleague_games, path = "euroleague_games.csv")write_csv(euroleague_players, path = "euroleague_players.csv")

# -----

# Eurocup seasons available and corresponding games numbers:eurocup_nums <- list(1:128,

2:186, 1:306, 1:306, 1:366, 1:157, 1:156, 1:156, 1:156,1:151, 1:326, 1:149, 1:149, 1:239, 1:209, 1:150)

names(eurocup_nums) <- 2017:2002


Height = NA, Date_birth = NA,Nationality = NA, Website_player = NA)

get_data <- get_games_rosters(competition = "Eurocup", nums = eurocup_nums,verbose = TRUE, r_user = "[email protected]",df0 = df0, df_bio0 = df_bio0)

eurocup_games <- get_data$df0eurocup_players <- get_data$df_bio0write_csv(eurocup_games, path = "eurocup_games.csv")write_csv(eurocup_players, path = "eurocup_players.csv")

## End(Not run)

get_heatmap_bb Basketball heatmap

Description

The heatmap created with this function allows the user to easily represent the stats for each player.The more intense the color, the more the player highlights in the statistic considered. The plot canbe ordered by any statistic. If all the statistics are represented, the offensive statistics are grouped inred, the defensive in green, the rest in purple and the advanced in pink. Otherwise, the default coloris red.

Usage

get_heatmap_bb(df_stats, team, levels_stats = NULL, stat_ord, base_size = 9, title)

Arguments


team Team.

levels_stats Statistics classified in several categories to plot. If this is NULL, all the statisticsare included in the data frame. Otherwise, the user can define a vector with thevariables to represent.

26 get_map_nats

stat_ord To sort the heatmap on one particular statistic.

base_size Sets the font size in the theme used. Default 9.

title Plot title.

Value

Graphical device.

Author(s)

This function has been created using the code from these websites: https://learnr.wordpress.com/2010/01/26/ggplot2-quick-heatmap-plotting/ and http://stackoverflow.com/questions/13016022/ggplot2-heatmaps-using-different-gradients-for-categories/13016912

Examples

## Not run:compet <- "ACB"df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)df2 <- do_stats(df1, "Total", "2017-2018", compet, "Regular Season")teams <- as.character(rev(sort(unique(df2$Team))))get_heatmap_bb(df2, teams[6], NULL, "MP", 9, paste(compet, "2017-2018", "Total", sep = " "))

## End(Not run)

get_map_nats Nationalities map

Description

A world map is represented. The countries from where there are players in the competition selectedare in green color.

Usage

get_map_nats(df_stats)

Arguments

df_stats Data frame with the statistics and the corrected nationalities.

Value

Graphical device.

https://learnr.wordpress.com/2010/01/26/ggplot2-quick-heatmap-plotting/

https://learnr.wordpress.com/2010/01/26/ggplot2-quick-heatmap-plotting/

http://stackoverflow.com/questions/13016022/ggplot2-heatmaps-using-different-gradients-for-categories/13016912

http://stackoverflow.com/questions/13016022/ggplot2-heatmaps-using-different-gradients-for-categories/13016912

get_pop_pyramid 27

Author(s)

Guillermo Vinue

See Also

do_map_nats

Examples

## Not run:compet <- "ACB"df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)df2 <- do_stats(df1, "Total", "2017-2018", compet, "Regular Season")get_map_nats(df2)

## End(Not run)

get_pop_pyramid Population pyramid

Description

This is the code to get a population pyramid with the number of both Spanish and foreigner playersalong the seasons for the ACB league. This aids in discussion of nationality imbalance.

Usage

get_pop_pyramid(df, title, language)

Arguments

df Data frame that contains the ACB players’ nationality.

title Title of the plot

language String, "eng" for English labels; "esp" for Spanish labels.

Value

Graphical device.

Author(s)

Guillermo Vinue

28 get_shooting_plot

Examples

## Not run:# Load the data_app_acb file with the ACB games# from seasons 1985-1986 to 2017-2018:load(url("http://www.uv.es/vivigui/softw/data_app_acb.RData"))title <- " Number of Spanish and foreign players along the ACB seasons \n Data from www.acb.com"get_pop_pyramid(data_app_acb, title, "eng")

## End(Not run)

get_shooting_plot Shooting plot

Description

This plot represents the number of shots attempted and scored by every player of the same team,together with the scoring percentage. The players are sortered by percentage.

Usage

get_shooting_plot(df_stats, team, type_shot, min_att, title, language)

Arguments


team Team.

type_shot Numeric with values 1-2-3: 1 refers to free throws, 2 refers to two point shotsand 3 refers to three points shots.

min_att Minimum number of attempts by the player to be represented in the plot.

title Plot title.

language Language labels. Current options are ’en’ for English and ’es’ for Spanish.

Value

Graphical device.

Author(s)

Guillermo Vinue

get_similar_players 29

Examples

## Not run:compet <- "ACB"df <- do_join_games_bio(compet, acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)df2 <- do_stats(df1, "Total", "2017-2018", compet, "Regular Season")get_shooting_plot(df2, "Valencia", 3, 1,

paste("Valencia", compet, "2017-2018", sep = " "), "en")

## End(Not run)

get_similar_players Similar players to archetypoids

Description

Similar players to the archetypoids computed with archetypoids according to a similarity thresh-old.

Usage

get_similar_players(atype, threshold, alphas, cases, data, variables, compet, season)

Arguments

atype Number assigned to the archetypoid (1:length(cases)) from which searching theplayers who most resemble to it.

threshold Similarity threshold.

alphas Alpha values of all the players.

cases Archetypoids.

data Data frame with the statistics.

variables Statistics used to compute the archetypoids.

compet Competition.

season Season.

Value

Data frame with the features of the similar players.

Author(s)

Guillermo Vinue

See Also

archetypoids

30 get_similar_teams

Examples

(s0 <- Sys.time())# Turn off temporarily some negligible warnings from the# archetypes package to avoid missunderstandings. The code works well.library(Anthropometry)df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)df1 <- do_add_adv_stats(df)df2 <- do_stats(df1, "Total", "2017-2018", "ACB", "Regular Season")df3 <- df2[which(df2$Position == "Guard")[1:31], c("MP", "PTS", "Name")]preproc <- preprocessing(df3[,1:2], stand = TRUE, percAccomm = 1)set.seed(4321)suppressWarnings(lass <- stepArchetypesRawData(preproc$data, 1:2,

numRep = 20, verbose = FALSE))res <- archetypoids(2, preproc$data, huge = 200, step = FALSE, ArchObj = lass,

nearest = "cand_ns", sequ = TRUE)# The S3 class of anthrCases from Anthropometry has been updated.cases <- anthrCases(res)df3[cases,] # https://github.com/r-quantities/units/issues/225alphas <- round(res$alphas, 4)df3_aux <- df2[which(df2$Position == "Guard")[1:31], ]get_similar_players(1, 0.99, alphas, cases, df3_aux, c("MP", "PTS"),

unique(df3_aux$Compet), unique(df3_aux$Season))s1 <- Sys.time() - s0s1

get_similar_teams Similar teams to archetypoids

Description

Similar teams to the archetypoids computed with archetypoids according to a similarity threshold.

Usage

get_similar_teams(atype, threshold, alphas, cases, data, variables)

Arguments

atype Number assigned to the archetypoid (1:length(cases)) from which searching theplayers who most resemble to it.

threshold Similarity threshold.

alphas Alpha values of all the players.

cases Archetypoids.

data Data frame with the statistics.

variables Statistics used to compute the archetypoids.

get_stats_seasons 31

Value

Data frame with the features of the similar teams.

Author(s)

Guillermo Vinue

See Also

archetypoids

Examples

## Not run:(s0 <- Sys.time())library(Anthropometry)df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)df$Compet <- "ACB"df_teams <- do_stats_teams(df, "2017-2018", "ACB", "Regular Season")df_team_total <- df_teams$df_team_total

df3 <- df_team_total[, c("PTS", "PTSrv", "Team")]preproc <- preprocessing(df3[,1:2], stand = TRUE, percAccomm = 1)set.seed(4321)lass <- stepArchetypesRawData(preproc$data, 1:2, numRep = 20, verbose = FALSE)res <- archetypoids(2, preproc$data, huge = 200, step = FALSE, ArchObj = lass,

nearest = "cand_ns", sequ = TRUE)cases <- anthrCases(res)df3[cases,]alphas <- round(res$alphas, 4)

get_similar_teams(1, 0.95, alphas, cases, df_team_total, c("PTS", "PTSrv"))s1 <- Sys.time() - s0s1

## End(Not run)

get_stats_seasons Season-by-season stats

Description

This function represents the average values of a set of statistics for certain players in every seasonwhere the players played. It gives an idea of the season-by-season performance.

Usage

get_stats_seasons(df, competition, player, variabs, type_season, add_text, show_x_axis)

32 get_table_results

Arguments


competition Competition.

player Players’s names.

variabs Vector with the statistics to plot.

type_season String with the round of competition, for example regular season or playoffs andso on.

add_text Boolean. Should text be added to the plot points?

show_x_axis Boolean. Should x-axis labels be shown in the plot?

Value

List with two elements:

• gg Graphical device.

• df_gg Data frame associated with the plot.

Author(s)

Guillermo Vinue

Examples

## Not run:competition <- "ACB"df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)df$Compet <- competitionplayer <- "Carroll, Jaycee"variabs <- c("GP", "MP", "PTS", "EFGPerc", "TRB", "AST", "TOV", "PIR")plot_yearly <- get_stats_seasons(df, competition, player, variabs, "All", TRUE, TRUE)plot_yearly$gg# There are only games from the regular season in this demo data frame.plot_yearly1 <- get_stats_seasons(df, competition, player, variabs, "Regular Season",

TRUE, TRUE)plot_yearly1$gg

## End(Not run)

get_table_results League cross table

Description

The league results are represented with a cross table.

join_players_bio_age_acb 33

Usage

get_table_results(df, competition, season)

Arguments


competition Competition.

season Season.

Value

List with these two elements:

• plot_teams Graphical device with the cross table.

• wins_teams Vector with the team wins.

Author(s)

Guillermo Vinue

Examples

## Not run:df <- do_join_games_bio("ACB", acb_games_1718, acb_players_1718)df$Compet <- "ACB"

gg <- get_table_results(df, "ACB", "2017-2018")

gg$wins_teamsgg$plot_teams

## End(Not run)

join_players_bio_age_acb

Join ACB games and players’ info

Description

This function joins the ACB games with the players’ bio and computes the players’ age at eachgame.

Usage

join_players_bio_age_acb(df_games, df_rosters)

34 join_players_bio_age_euro

Arguments



Value

Data frame.

Author(s)

Guillermo Vinue

See Also

do_join_games_bio

Examples

df <- join_players_bio_age_acb(acb_games_1718, acb_players_1718)

join_players_bio_age_euro

Join Euroleague and Eurocup games and players’ info

Description

This function joins the Euroleague/Eurocup games with the players’ bio and computes the players’age at each game.

Usage

join_players_bio_age_euro(df_games, df_rosters)

Arguments



Value

Data frame.

Author(s)

Guillermo Vinue

scraping_games_acb 35

See Also

do_join_games_bio

Examples

df <- join_players_bio_age_euro(euroleague_games_1718, euroleague_players_1718)

scraping_games_acb ACB player game finder data

Description

This function allows us to get all the player game finder data for all the desired ACB seasonsavailable from: http://www.acb.com.

Usage

scraping_games_acb(type_league, nums, year, verbose = TRUE,accents = FALSE, r_user = "[email protected]")

Arguments


nums Numbers corresponding to the website to scrape.

year Season, e.g. 2017-2018.


accents Should we keep the Spanish accents? The recommended option is to removethem, so default FALSE.


Details

The official website of the Spanish basketball league ACB presents the statistics of each game in aphp website, such as: http://www.acb.com/fichas/LACB62090.php.

Note that for example http://www.acb.com/fichas/LACB60315.php doesn’t exist, so for these casesis where we can use the httr package.

In https://www.uv.es/vivigui/docs/acb_scraping.pdf a document is available with the ex-act numbers xxxxx related to http://www.acb.com/fichas/LACBxxxxx.php for some seasons.

Value

A data frame with the player game finder data.

http://www.acb.com

http://www.acb.com/fichas/LACB62090.php

https://www.uv.es/vivigui/docs/acb_scraping.pdf

36 scraping_games_euro

Note

In addition to use the email address to stay identifiable, the function also contains two headersregarding the R platform and version used.

Furthermore, even though in the robots.txt file at http://www.acb.com/robots.txt, there is noinformation about scraping limitations and all robots are allowed to have complete access, the func-tion also includes the command Sys.sleep(2) to pause between requests for 2 seconds. In thisway, we don’t bother the server with multiple requests and we do carry out a friendly scraping.

Author(s)

Guillermo Vinue

See Also

do_scraping_games

Examples

## Not run:# Not needed to scrape every time the package is checked, built and installed.df1 <- scraping_games_acb(type_league = "ACB", nums = 62001:62002, year = "2017-2018",

verbose = TRUE, accents = FALSE,r_user = "[email protected]")

## End(Not run)

scraping_games_euro Euroleague and Eurocup player game finder data

Description

This function allows us to get all the player game finder data for all the desired Euroleague and Eu-rocup seasons available from http://www.euroleague.net/main/results/ and http://www.eurocupbasketball.com/eurocup/games/results, respectively.

Usage

scraping_games_euro(competition, nums, year, verbose = TRUE,r_user = "[email protected]")

Arguments

competition String. Options are "Euroleague" and "Eurocup".


year Year when the season starts. 2017 refers to 2017-2018 and so on.


http://www.acb.com/robots.txt

http://www.euroleague.net/main/results/

http://www.eurocupbasketball.com/eurocup/games/results

http://www.eurocupbasketball.com/eurocup/games/results

scraping_games_euro 37


Details

See the examples in get_games_rosters to see the game numbers to scrape in each season.

Value

A data frame with the player game finder data.

Note


Furthermore, in the robots.txt file located at http://www.euroleague.net/robots.txt and https://www.eurocupbasketball.com/robots.txt there is the Crawl-delay field which asks crawlersto pause between requests for 15 seconds. This is done by adding to the function the commandSys.sleep(15).

Author(s)

Guillermo Vinue

See Also

do_scraping_games

Examples

## Not run:# Not needed to scrape every time the package is checked, built and installed.# It takes 15 seconds as it is required in http://www.euroleague.net/robots.txtdf1 <- do_scraping_games(competition = "Euroleague", nums = 1:2,

year = "2017", verbose = TRUE, r_user ="[email protected]")

## End(Not run)

http://www.euroleague.net/robots.txt

https://www.eurocupbasketball.com/robots.txt


38 scraping_rosters_acb

scraping_rosters_acb ACB players’ profile

Description

This function allows us to obtain the basic information of each player, including his birth date.Then, we will be able to compute the age that each player had in the date that he played each game.The website used to collect information is https://www.acb.com.

Usage

scraping_rosters_acb(pcode, verbose = TRUE, accents = FALSE,r_user = "[email protected]")

Arguments



accents Should we keep the Spanish accents? The recommended option is to removethem, so default FALSE.

r_user Email user to identify the user when doing web scraping. This is a polite wayto do web scraping and to certify that the user is working as transparently aspossible with a research purpose.

Details

Some players have a particular licence, which does not necessarily match with their nationality, inorder not to be considered as a foreign player, according to the current ACB rules.

Value

Data frame with eight columns:

• CombinID: Unique ID to identify the players.

• Player: Player’s name.

• Position: Player’s position on the court.

• Height: Player’s height.

• Date_birth: Player’s birth date.

• Nationality: Player’s nationality.

• Licence: Player’s licence.

• Website_player: Website.

https://www.acb.com

scraping_rosters_euro 39

Note


Furthermore, even though in the robots.txt file at http://www.acb.com/robots.txt, there is noinformation about scraping limitations and all robots are allowed to have complete access, the func-tion also includes the command Sys.sleep(2) to pause between requests for 2 seconds. In thisway, we don’t bother the server with multiple requests and we do carry out a friendly scraping.

Author(s)

Guillermo Vinue

See Also

do_scraping_rosters

Examples

## Not run:# Not needed to scrape every time the package is checked, built and installed.df_bio <- scraping_rosters_acb("56C", verbose = TRUE, accents = FALSE,

r_user = "[email protected]")

## End(Not run)

scraping_rosters_euro Euroleague and Eurocup players’ profile

Description

This function allows us to obtain the basic information of each Euroleague/Eurocup player, includ-ing his birth date. Then, we will be able to compute the age that each player had in the date that heplayed each game. The websites used to collect information are https://www.euroleague.netand https://www.eurocupbasketball.com.

Usage

scraping_rosters_euro(competition, pcode, year, verbose = TRUE,r_user = "[email protected]")

Arguments

competition String. Options are "Euroleague" and "Eurocup".


year Year when the season starts. 2017 refers to 2017-2018 and so on.


http://www.acb.com/robots.txt

https://www.euroleague.net

https://www.eurocupbasketball.com

40 scraping_rosters_euro

r_user Email user to identify the user when doing web scraping. This is a polite wayto do web scraping and to certify that the user is working as transparently aspossible with a research purpose.

Value

Data frame with seven columns:

• CombinID: Unique ID to identify the players.

• Player: Player’s name.

• Position: Player’s position on the court.

• Height: Player’s height.

• Date_birth: Player’s birth date.

• Nationality Player’s nationality.

• Website_player: Website.

Note


Furthermore, in the robots.txt file located at http://www.euroleague.net/robots.txt and https://www.eurocupbasketball.com/robots.txt there is the Crawl-delay field which asks crawlersto pause between requests for 15 seconds. This is done by adding to the function the commandSys.sleep(15).

Author(s)

Guillermo Vinue

See Also

do_scraping_rosters

Examples

## Not run:# Not needed to scrape every time the package is checked, built and installed.# It takes 15 seconds as it is required in http://www.euroleague.net/robots.txtdf_bio <- scraping_rosters_euro("Euroleague", "005791", "2017", verbose = TRUE,

r_user = "[email protected]")

## End(Not run)

http://www.euroleague.net/robots.txt



Index

∗Topic BAwiRBAwiR-package, 3

∗Topic datasetsacb_games_1718, 4acb_players_1718, 5eurocup_games_1718, 16eurocup_players_1718, 16euroleague_games_1718, 17euroleague_players_1718, 17

acb_games_1718, 4acb_players_1718, 5archetypoids, 29–31

BAwiR-package, 3

capit_two_words, 5, 18

do_add_adv_stats, 6, 7, 11do_EPS, 6, 7, 11do_four_factors_df, 8, 21do_join_games_bio, 9, 34, 35do_map_nats, 10, 27do_OE, 6, 7, 11do_scraping_games, 12, 36, 37do_scraping_rosters, 13, 39, 40do_stats, 14do_stats_teams, 15

eurocup_games_1718, 16eurocup_players_1718, 16euroleague_games_1718, 17euroleague_players_1718, 17

get_barplot_monthly_stats, 18get_bubble_plot, 19get_four_factors_plot, 9, 21get_games_rosters, 22, 37get_heatmap_bb, 25get_map_nats, 10, 26get_pop_pyramid, 27

get_shooting_plot, 28get_similar_players, 29get_similar_teams, 30get_stats_seasons, 31get_table_results, 32

join_players_bio_age_acb, 9, 33join_players_bio_age_euro, 9, 34

percentilsArchetypoid, 19, 20

scraping_games_acb, 12, 14, 35scraping_games_euro, 12, 36scraping_rosters_acb, 38scraping_rosters_euro, 14, 39

41

Documents

Package ‘BAwiR’of their respectives robots.txt ﬁles, when available. Tools for visualization include a popula-tion pyramid, 2D plots, circular plots of players' percentiles,